@inproceedings{roul-etal-2024-multi,
    title = "Multi-document Summarization by Ensembling of Scoring and Topic Modeling Techniques",
    author = "Roul, Rajendra Kumar  and
      Navpreet  and
      Nalband, Saif",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.icon-1.69/",
    pages = "588--592",
    abstract = "With the growing volume of text, finding relevant information is increasingly difficult. Automatic Text Summarization (ATS) addresses this by efficiently extracting relevant content from large document collections. Despite progress, ATS faces challenges like managing long, repetitive sentences, preserving coherence, and maintaining semantic alignment. This work introduces an extractive summarization approach based on topic modeling to address these issues. The proposed method produces summaries with representative sentences, reduced redundancy, concise content, and strong semantic consistency. Its effectiveness, demonstrated through experiments on DUC datasets, outperforms state-of-the-art techniques."
}